Sramana: Our audience is very familiar with price comparison engines as consumers and as businesses. The new perspective you are bringing is the experience of working with price comparison engines as an e-commerce site. You said that you submit your site and a structured data set to the price comparison engine. I thought that price comparison engines crawled websites to create those datasets.

Pavel Sokolovsky: I am not familiar with price comparison engines that crawl sites, but I would assume they also use a lead generation model. The sites we use require us to feed them from our product database. We upload a list of products and their properties to them via FTP.

Once we do that, we have to estimate a cost per click that we are willing to pay. Some of the comparison engines have a flat fee model and others have varying fees based on category. Finally, some sites use a bidding system to determine the cost per click.

There are a couple of levers that you can pull while you are working with those price comparison engines. First, if they offer bidding, then you can control where you rank in comparison to your competitors. In other cases, the search functionality of the price comparison engines is not perfect. We often get mistargeted clicks. If somebody searches a term, it may very well bring up our product even though a human would not think that it is a relevant search result. However, humans will still click and follow through on that product because it’s so easy to do. There is a high potential for poorly managed feeds to get clicked that will never result in a sell.

For example, if somebody is searching for a kitchen appliance that makes hot water for tea or coffee, they might use search terms like hot water boiler. That search term will also bring up a hot water boiler for a central HVAC system for a home. When they click on that link, it will cost us money. The products are obviously very far apart and the person is not going to be in the market for an HVAC component, but we still pay for the cost of that click.

We have to manage our products defensively. We have to algorithmically determine when those false links are clicked. We have built proprietary algorithms over time to determine when that is happening. When that occurs we have two levers we can use. One, we can lower the price per click. The other is to remove that product from that shopping engine. We do that on a large scale so that we can manage our advertising revenue on each one of our channels and match that against the revenue those channels bring in. That lets us keep our business profitable.